scholarly journals PIV measurement of buffer and logarithmic layers with detached eddies which mimics the neutral atmospheric surface layer

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Yasuo Hattori ◽  
Hitoshi Suto ◽  
Keisuke Nakao ◽  
Hiromaru Hirakuchi
Keyword(s):  
Surface Layer ◽  
Atmospheric Surface Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Long Distance ◽  
Flat Surfaces ◽  
Piv Measurement ◽  
Reynolds Number Effects ◽  
Underlying Surface Conditions ◽  
Neutral Conditions

Accurate comprehension of turbulence characteristics in the atmospheric surface layer (ASL) under near neutral conditions, which is a lower part of the atmospheric boundary layer and a very high-Re number flow, is critically required in view of the increasing and broadening use of numerical weather prediction models. The models need to estimate turbulence fluxes of momentum, heat and moisture in the ASL as boundary conditions. On the other hand, observations (Högström 1990, Drobinski et al. 2007) have revealed that the fluxes under near-neutral conditions are often inconsistent with Monin-Obukhof theory, which has been widely used in models. The observations were conducted over flat surfaces with homogeneous roughness, and thus the violation from the theory might not be due to the underlying surface conditions. Thus, aiming to investigate an origin of the violation from the theory, we have carried out a wind tunnel experiment on the logarithmic layer along a smooth flat wall with a larger-scale disturbance, which mimics the near-neutral atmospheric surface layer (Hattori et al. 2010). In the present study, we especially examine a PIV measurement with a long-distance microscope lens to discuss the interaction of turbulences structures between buffer and logarithmic layers, which must give a clue on Reynolds number effects

Ensemble Data Assimilation to Characterize Surface-Layer Errors in Numerical Weather Prediction Models

2013 ◽  
Vol 141 (6) ◽  
pp. 1804-1821 ◽  
Author(s):  
J. P. Hacker ◽  
W. M. Angevine
Keyword(s):  
Surface Layer ◽  
Data Assimilation ◽  
Atmospheric Surface Layer ◽  
Weather Prediction ◽  
Model Error ◽  
Time Of Day ◽  
Turbulent Heat ◽  
Model Errors ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Abstract Experiments with the single-column implementation of the Weather Research and Forecasting Model provide a basis for deducing land–atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land–atmosphere interface and roughness sublayer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following the Monin–Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented Atmospheric Radiation Measurement (ARM) Central Facility near Lamont, Oklahoma. One-hour errors in predicted observations are systematically small but nonzero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15 m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land–atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural errors or further parametric errors that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing the potential for assimilating common in situ observations as an inexpensive framework for deducing and isolating model errors.

Structure of weather prediction errors in stably-stratified atmospheric conditions

2020 ◽  
Author(s):  
Igor Esau ◽  
Stephen Outten ◽  
Mikhail Tolstykh
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Turbulent Mixing ◽  
Prediction Models ◽  
Numerical Models ◽  
Temporal Structure ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Prediction Errors ◽  
Atmospheric Conditions

<p>Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.</p>

scholarly journals Nondimensional Wind and Temperature Profiles in the Atmospheric Surface Layer over the Hinterland of the Taklimakan Desert in China

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Yongqiang Liu ◽  
Ali Mamtimin ◽  
Wen Huo ◽  
Xinghua Yang ◽  
Xinchun Liu ◽  
...  
Keyword(s):  
Surface Layer ◽  
Atmospheric Surface Layer ◽  
Stable Stratification ◽  
Taklimakan Desert ◽  
Temperature Profiles ◽  
The Taklimakan Desert ◽  
Arid Desert ◽  
Neutral Conditions ◽  
Good Agreement ◽  
Neutral Stratification

Observed turbulent fluxes, wind, and temperature profiles at Tazhong station over the hinterland of the Taklimakan Desert in China have been analyzed to evaluate empirical parameters used in the profile functions of desert surface layer. The von Kármán constant derived from our observations is about 0.396 in near-neutral stratification, which is in good agreement with many other studies for different underlying surface. In our analysis, the turbulent Prandtl number is about 0.75 in near-neutral conditions. For unstable range, the nondimensional wind and temperature profile functions are best fitted by the exponents of −1/4 and −1/2, respectively. The linear relations still hold for stable stratification in this extremely arid desert. However, the parameters used in their profile functions need to be revised to be applicable for desert surfaces.

scholarly journals Sensitivity of a Mediterranean Tropical-Like Cyclone to Physical Parameterizations

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 436 ◽  
Author(s):  
Ioannis Pytharoulis ◽  
Stergios Kartsios ◽  
Ioannis Tegoulias ◽  
Haralambos Feidas ◽  
Mario Miglietta ◽  
...  
Keyword(s):  
Surface Layer ◽  
Prediction Models ◽  
Deep Convection ◽  
Weather Prediction ◽  
Boundary Surface ◽  
Shallow Convection ◽  
Roughness Lengths ◽  
Important Challenge ◽  
The One ◽  
Physical Parameterizations

The accurate prediction of Mediterranean tropical-like cyclones, or medicanes, is an important challenge for numerical weather prediction models due to their significant adverse impact on the environment, life, and property. The aim of this study is to investigate the sensitivity of an intense medicane, which formed south of Sicily on 7 November 2014, to the microphysical, cumulus, and boundary/surface layer schemes. The non-hydrostatic Weather Research and Forecasting model (version 3.7.1) is employed. A symmetric cyclone with a deep warm core, corresponding to a medicane, develops in all of the experiments, except for the one with the Thompson microphysics. There is a significant sensitivity of different aspects of the simulated medicane to the physical parameterizations. Its intensity is mainly influenced by the boundary/surface layer scheme, while its track is mainly influenced by the representation of cumulus convection, and its duration is mainly influenced by microphysical parameterization. The modification of the drag coefficient and the roughness lengths of heat and moisture seems to improve its intensity, track, and duration. The parameterization of shallow convection, with explicitly resolved deep convection, results in a weaker medicane with a shorter lifetime. An optimum combination of physical parameterizations in order to simulate all of the characteristics of the medicane does not seem to exist.

Toward the Assimilation of the Atmospheric Surface Layer Using Numerical Weather Prediction and Radar Clutter Observations

2013 ◽  
Vol 52 (10) ◽  
pp. 2345-2355 ◽  
Author(s):  
Ali Karimian ◽  
Caglar Yardim ◽  
Tracy Haack ◽  
Peter Gerstoft ◽  
William S. Hodgkiss ◽  
...  
Keyword(s):  
Surface Layer ◽  
Relative Humidity ◽  
Numerical Weather Prediction ◽  
Atmospheric Surface Layer ◽  
Weather Prediction ◽  
Propagation Model ◽  
Near Surface ◽  
Radar Clutter ◽  
Numerical Weather ◽  
Evaporation Duct

AbstractRadio wave propagation on low-altitude paths over the ocean above 2 GHz is significantly affected by negative refractivity gradients in the atmospheric surface layer, which form what is often referred to as an evaporation duct (ED). Refractivity from clutter (RFC) is an inversion approach for the estimation of the refractivity profile from radar clutter, and RFC-ED refers to its implementation for the case of evaporation ducts. An approach for fusing RFC-ED output with evaporation duct characterization that is based on ensemble forecasts from a numerical weather prediction (NWP) model is examined here. Three conditions of air–sea temperature difference (ASTD) are examined. Synthetic radar clutter observations are generated using the Advanced Propagation Model. The impacts of ASTD on the evaporation duct refractivity profile, atmospheric parameter inversion, and propagation factor distributions are studied. Relative humidity at a reference height and ASTD are identified as state variables. Probability densities from NWP ensembles, RFC-ED, and joint inversions are compared. It is demonstrated that characterization of the near-surface atmosphere by combining RFC-ED and NWP reduces the estimation uncertainty of ASTD and relative humidity in an evaporation duct, with respect to using either method alone.

An atmospheric surface layer study: The Idealized horizontal Planar Array experiment for Quantifying Surface Heterogeneity (IPAQS)

2020 ◽  
Author(s):  
Travis Morrison ◽  
Marc Calaf ◽  
Eric Pardyjak ◽  
Marcus Hultmark ◽  
Chad Higgins ◽  
...  
Keyword(s):  
Surface Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Heterogeneity ◽  
Internal Boundary ◽  
Valuable Insight ◽  
Sonic Anemometers ◽  
Wide Range ◽  
Planar Array ◽  
Scale Turbulence

<p>Numerical weather prediction models rely heavily on boundary-layer theories, which poorly capture the interactions between the Earth’s heterogeneous surface and the internal boundary layers aloft. Further, in relation to these theories, there remains outstanding questions that still require new understanding, such as the closure of the surface energy balance, advection quantification, and surface-flux interaction. We hypothesize that under certain conditions of unstable and neutral stratification, surface thermal heterogeneities can significantly influence the flow structure and alter momentum and scalar transport. To be able to access this hypothesis, we designed the Idealized horizontal Planar Array experiment for Quantifying Surface heterogeneity (IPAQS). IPAQS took place during the summers of 2018 and 2019 at the Great Salt Lake Desert playa in western Utah at the U.S. Army Dugway Proving Ground’s Surface Layer Turbulence and Environmental Test (SLTEST) facility. The site is characterized by a long uninterrupted fetch with uniform surface roughness and large thermal and moisture heterogeneities covering a wide range of scales. Observations were made with an array of 2-m high, temporally-synchronized, fast-response sonic anemometers, and finewire thermocouples, which were deployed on a coarse grid covering an area of 800 m x 800 m with 200-m spacing. Results provide valuable insight into the spatial and temporal evolution of the flow. Fine-scale turbulence was measured using Nano-Scale Thermal Anemometry Probes (NSTAP). Meanwhile, larger-scale turbulence was captured with Doppler wind LiDARs. Presented is an overview of the experiment and initial results.</p>

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

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